Proceedings Paper

A vision-based system has been developed for industrial lace cutting. On-line pattern recognition is performed to detect the cutting path (river). The river is vectorized and passed to a numerical control system that drives a cutter. Since lace is subject to stretch as it is passed through the machine, vision is used to detect changes and provide the necessary cutter path compensation. Detection is carried out in real time and changes are fed back to the control subsystem. Various ways of change detection are investigated. The most effective method is found to be that of frame comparison using prior knowledge. Prior knowledge is used to enable rapid isolation of the river and detection of changes in the river pattern. Several experiments have been carried out using lace patterns of varying complexity. The experimental results are presented. A working prototype is described. The effectiveness of the optimization techniques employed to meet the real-time constraints is evaluated.